DataStax Developer Blog

In this post, we explore a quick look at our DataStax Academy Developer Days. We've held a few of the Developer Days already in New York City and Washington DC, and just wrapped up two more Chicago, Illinois and Dallas, Texas! Next up are London and Paris, which we're really looking forward to. Teaching developers and practitioners at the Developer Days have been just as rewarding for us as getting a chance to learn about what everybody out there in the industry is working on!

Missed Graph Day 2018? How sad! But here is a blog to fill you in on some of the fantastic talks that were featured at Graph Day 2018!

A natural language processing example using DataStax Enterprise Analytics with Apache Cassandra andApache Spark, Python, Jupyter Notebooks, Twitter API, Pattern (python package), and Sentiment Analysis

Graph Databases are really effective when it comes to working with highly connected data and getting value based on relationships, as we detailed in this previous blogpost. This article focuses on integrating graph databases with web applications to implement CRUD operations, pattern detection, and visualization in the user interface. Part 1 is dedicated to environment setup and CRUD operations, and Part 2 will dig into the user interface. Let's get our hands dirty.

This article discusses how you can leverage the paging features of Apache Cassandra to support a great user experience in your application, using examples from the KillrVideo reference application.

This post is the second and last part of a series digging into integration of Graph Databases with web applications. In Part 1, we created a data access object (DAO) to implement basic CRUD operations for both vertices and edges. Here, we will have a closer look at graph visualization in user interfaces. One of the coolest features of graph is the capability to browse the data to identiy patterns and extract information from relationships. DataStax Studio provides with great visuat rendering but, why not having the same visualization in your own applications?

Want to take a quick tour of several quick ways to get started with DataStax Enterprise? Well, this is your guide!

In this article I've written up a whirlwind tour of the Google Cloud Platform Marketplace and Azure Marketplace options for DataStax Enterprise. Both of these solutions can get a cluster built in short order across multiple regions with plenty of performance power and resiliency. I then step through resources and steps to get started with a Docker deployed solution locally for testing and development. But the end of this article you'll be ready to get going quickly with DataStax Enterprise through a number of quick deployment options.

In this article, Volkan Civelek examines the pros and cons of using public cloud database services, the case for using DataStax Enterprise as a data layer across multiple clouds, and advice on running DSE in Kubernetes.

We've recently renamed our Technical Evangelist team at DataStax. Read this post to find out why and what it means for you.